OpenRaiser/Intern-Atlas huggingface dataset:
Can Eye Movement Data Be Used As Ground Truth For Word Embeddings Evaluation?
Venue: arXiv cs.CL 2018
Year: 2018

In recent years a certain success in the task of modeling lexical semantics was obtained with distributional semantic models. Nevertheless, the scientific community is still unaware what is the most reliable evaluation method for these models. Some researchers argue that the only possible gold standard could be obtained from neuro-cognitive resources that store information about human cognition. One of such resources is eye movement data on silent reading. The goal of this work is to test the hypothesis of whether such data could be used to evaluate distributional semantic models on different languages. We propose experiments with English and Russian eye movement datasets (Provo Corpus, GECO and Russian Sentence Corpus), word vectors (Skip-Gram models trained on national corpora and Web corpora) and word similarity datasets of Russian and English assessed by humans in order to find the existence of correlation between embeddings and eye movement data and test the hypothesis that this correlation is language independent. As a result, we found that the validity of the hypothesis being tested could be questioned.
Siamese Generative Adversarial Privatizer for Biometric Data
Venue: arXiv cs.CV 2018
Year: 2018

State-of-the-art machine learning algorithms can be fooled by carefully crafted adversarial examples. As such, adversarial examples present a concrete problem in AI safety. In this work we turn the tables and ask the following question: can we harness the power of adversarial examples to prevent malicious adversaries from learning identifying information from data while allowing non-malicious entities to benefit from the utility of the same data? For instance, can we use adversarial examples to anonymize biometric dataset of faces while retaining usefulness of this data for other purposes, such as emotion recognition? To address this question, we propose a simple yet effective method, called Siamese Generative Adversarial Privatizer (SGAP), that exploits the properties of a Siamese neural network to find discriminative features that convey identifying information. When coupled with a generative model, our approach is able to correctly locate and disguise identifying information, while minimally reducing the utility of the privatized dataset. Extensive evaluation on a biometric dataset of fingerprints and cartoon faces confirms usefulness of our simple yet effective method.
A Call for Clarity in Reporting BLEU Scores
Venue: arXiv cs.CL 2018
Year: 2018

The field of machine translation faces an under-recognized problem because of inconsistency in the reporting of scores from its dominant metric. Although people refer to "the" BLEU score, BLEU is in fact a parameterized metric whose values can vary wildly with changes to these parameters. These parameters are often not reported or are hard to find, and consequently, BLEU scores between papers cannot be directly compared. I quantify this variation, finding differences as high as 1.8 between commonly used configurations. The main culprit is different tokenization and normalization schemes applied to the reference. Pointing to the success of the parsing community, I suggest machine translation researchers settle upon the BLEU scheme used by the annual Conference on Machine Translation (WMT), which does not allow for user-supplied reference processing, and provide a new tool, SacreBLEU, to facilitate this.
Optimized Deployment of Network Function for Resource Pooling Switch
Venue: arXiv cs.NI 2018
Year: 2018

The disadvantages of the combination of traditional switches and middleboxes have being exposed under the condition of increasingly various network function demands,such as function flexibility, performance scalability and resource utilization. To solve this problem, we design Resource Pooling Switch Architecture (RPSA), which separates some non-essential functions from line card and allocate them in Network Function Pool (NFP) to provide flexible services for data plane in the form of Service Function Chains (SFC). As the performance of the whole system could be decided by whether function deployment is reasonable or not, we purpose heuristic algorithm called Modified Fiduccia-Mattheyses based Two Phase Algorithm (MFMTP) to optimize the deployment of functions. The simulation results show that this algorithm performs well in throughput and convergence.
Explaining hyperspectral imaging based plant disease identification: 3D CNN and saliency maps
Venue: arXiv cs.CV 2018
Year: 2018

Our overarching goal is to develop an accurate and explainable model for plant disease identification using hyperspectral data. Charcoal rot is a soil borne fungal disease that affects the yield of soybean crops worldwide. Hyperspectral images were captured at 240 different wavelengths in the range of 383 - 1032 nm. We developed a 3D Convolutional Neural Network model for soybean charcoal rot disease identification. Our model has classification accuracy of 95.73\% and an infected class F1 score of 0.87. We infer the trained model using saliency map and visualize the most sensitive pixel locations that enable classification. The sensitivity of individual wavelengths for classification was also determined using the saliency map visualization. We identify the most sensitive wavelength as 733 nm using the saliency map visualization. Since the most sensitive wavelength is in the Near Infrared Region(700 - 1000 nm) of the electromagnetic spectrum, which is also the commonly used spectrum region for determining the vegetation health of the plant, we were more confident in the predictions using our model.
Representing the Unknown - Impact of Uncertainty on the Interaction between Decision Making and Trajectory Generation
Venue: arXiv cs.SY 2018
Year: 2018

Even though motion planning for automated vehicles has been extensively discussed for more than two decades, it is still a highly active field of research with a variety of different approaches having been published in the recent years. When considering the market introduction of SAE Level 3+ vehicles, the topic of motion planning will most likely be subject to even more detailed discussions between safety and user acceptance. This paper shall discuss parameters of the motion planning problem and requirements to an environment model. The focus is put on the representation of different types of uncertainty at the example of sensor occlusion, arguing the importance of a well-defined interface between decision making and trajectory generation.
Mask-aware Photorealistic Face Attribute Manipulation
Venue: arXiv cs.CV 2018
Year: 2018

The task of face attribute manipulation has found increasing applications, but still remains challenging with the requirement of editing the attributes of a face image while preserving its unique details. In this paper, we choose to combine the Variational AutoEncoder (VAE) and Generative Adversarial Network (GAN) for photorealistic image generation. We propose an effective method to modify a modest amount of pixels in the feature maps of an encoder, changing the attribute strength continuously without hindering global information. Our training objectives of VAE and GAN are reinforced by the supervision of face recognition loss and cycle consistency loss for faithful preservation of face details. Moreover, we generate facial masks to enforce background consistency, which allows our training to focus on manipulating the foreground face rather than background. Experimental results demonstrate our method, called Mask-Adversarial AutoEncoder (M-AAE), can generate high-quality images with changing attributes and outperforms prior methods in detail preservation.
Learning Software Constraints via Installation Attempts
Venue: arXiv cs.SE 2018
Year: 2018

Modern software systems are expected to be secure and contain all the latest features, even when new versions of software are released multiple times an hour. Each system may include many interacting packages. The problem of installing multiple dependent packages has been extensively studied in the past, yielding some promising solutions that work well in practice. However, these assume that the developers declare all the dependencies and conflicts between the packages. Oftentimes, the entire repository structure may not be known upfront, for example when packages are developed by different vendors. In this paper, we present algorithms for learning dependencies, conflicts and defective packages from installation attempts. Our algorithms use combinatorial data structures to generate queries that test installations and discover the entire dependency structure. A query that the algorithms make corresponds to trying to install a subset of packages and getting a Boolean feedback on whether all constraints were satisfied in this subset. Our goal is to minimize the query complexity of the algorithms. We prove lower and upper bounds on the number of queries that these algorithms require to make for different settings of the problem.
Improved Local Search Based Approximation Algorithm for Hard Uniform Capacitated k-Median Problem
Venue: arXiv cs.DS 2018
Year: 2018

In this paper, we study the hard uniform capacitated $k$- median problem using local search heuristic. Obtaining a constant factor approximation for the \ckm problem is open. All the existing solutions giving constant-factor approximation, violate at least one of the cardinality and the capacity constraints. All except Koruplou et al are based on LP-relaxation. We give $(3+ε)$ factor approximation algorithm for the problem violating the cardinality by a factor of $8/3 \approx 2.67$. There is a trade-off between the approximation factor and the cardinality violation between our work and the existing work. Koruplou et al gave $(1 + α)$ approximation factor with $(5 + 5/α)$ factor loss in cardinality using local search paradigm. Though the approximation factor can be made arbitrarily small, cardinality loss is at least $5$. On the other hand, we improve upon the results in [capkmGijswijtL2013],[capkmshili2014], [Lisoda2016] in terms of factor-loss though the cardinality loss is more in our case. Also, these results are obtained using LP-rounding, some of them being strengthened, whereas local search techniques are simple to apply and have been shown to perform well in practice via empirical studies. We extend the result to hard uniform capacitated $k$-median with penalties. To the best of our knowledge, ours is the first result for the problem.
Solving Horn Clauses on Inductive Data Types Without Induction
Venue: arXiv cs.LO 2018
Year: 2018

We address the problem of verifying the satisfiability of Constrained Horn Clauses (CHCs) based on theories of inductively defined data structures, such as lists and trees. We propose a transformation technique whose objective is the removal of these data structures from CHCs, hence reducing their satisfiability to a satisfiability problem for CHCs on integers and booleans. We propose a transformation algorithm and identify a class of clauses where it always succeeds. We also consider an extension of that algorithm, which combines clause transformation with reasoning on integer constraints. Via an experimental evaluation we show that our technique greatly improves the effectiveness of applying the Z3 solver to CHCs. We also show that our verification technique based on CHC transformation followed by CHC solving, is competitive with respect to CHC solvers extended with induction. This paper is under consideration for acceptance in TPLP.
Automated Big Traffic Analytics for Cyber Security
Venue: arXiv cs.CR 2018
Year: 2018

Network traffic analytics technology is a cornerstone for cyber security systems. We demonstrate its use through three popular and contemporary cyber security applications in intrusion detection, malware analysis and botnet detection. However, automated traffic analytics faces the challenges raised by big traffic data. In terms of big data's three characteristics --- volume, variety and velocity, we review three state of the art techniques to mitigate the key challenges including real-time traffic classification, unknown traffic classification, and efficiency of classifiers. The new techniques using statistical features, unknown discovery and correlation analytics show promising potentials to deal with big traffic data. Readers are encouraged to devote to improving the performance and practicability of automatic traffic analytic in cyber security.
Toward a Better Understanding of How to Develop Software Under Stress - Drafting the Lines for Future Research
Venue: arXiv cs.SE 2018
Year: 2018

The software is often produced under significant time constraints. Our idea is to understand the effects of various software development practices on the performance of developers working in stressful environments, and identify the best operating conditions for software developed under stressful conditions collecting data through questionnaires, non-invasive software measurement tools that can collect measurable data about software engineers and the software they develop, without intervening their activities, and biophysical sensors and then try to recreated also in different processes or key development practices such conditions.
SITAN: Services for Fault-Tolerant Ad Hoc Networks with Unknown Participants
Venue: arXiv cs.DC 2018
Year: 2018

The evolution of mobile devices with various capabilities (e.g., smartphones and tablets), together with their ability to collaborate in impromptu ad hoc networks, opens new opportunities for the design of innovative distributed applications. The development of these applications needs to address several difficulties, such as the unreliability of the network, the imprecise set of participants, or the presence of malicious nodes. In this paper we describe a middleware, called SITAN, that offers a number of communication, group membership and coordination services specially conceived for these settings. These services are implemented by a stack of Byzantine fault-tolerant protocols, enabling applications that are built on top of them to operate correctly despite the uncertainty of the environment. The protocol stack was implemented in Android and NS-3, which allowed the experimentation in representative scenarios. Overall, the results show that the protocols are able to finish their execution within a small time window, which is acceptable for various kinds of applications.
Keep it Unreal: Bridging the Realism Gap for 2.5D Recognition with Geometry Priors Only
Venue: arXiv cs.CV 2018
Year: 2018

With the increasing availability of large databases of 3D CAD models, depth-based recognition methods can be trained on an uncountable number of synthetically rendered images. However, discrepancies with the real data acquired from various depth sensors still noticeably impede progress. Previous works adopted unsupervised approaches to generate more realistic depth data, but they all require real scans for training, even if unlabeled. This still represents a strong requirement, especially when considering real-life/industrial settings where real training images are hard or impossible to acquire, but texture-less 3D models are available. We thus propose a novel approach leveraging only CAD models to bridge the realism gap. Purely trained on synthetic data, playing against an extensive augmentation pipeline in an unsupervised manner, our generative adversarial network learns to effectively segment depth images and recover the clean synthetic-looking depth information even from partial occlusions. As our solution is not only fully decoupled from the real domains but also from the task-specific analytics, the pre-processed scans can be handed to any kind and number of recognition methods also trained on synthetic data. Through various experiments, we demonstrate how this simplifies their training and consistently enhances their performance, with results on par with the same methods trained on real data, and better than usual approaches doing the reverse mapping.
A More Fine-Grained Complexity Analysis of Finding the Most Vital Edges for Undirected Shortest Paths
Venue: arXiv cs.CC 2018
Year: 2018

We study the NP-hard Shortest Path Most Vital Edges problem arising in the context of analyzing network robustness. For an undirected graph with positive integer edge lengths and two designated vertices $s$ and $t$, the goal is to delete as few edges as possible in order to increase the length of the (new) shortest $st$-path as much as possible. This scenario has been studied from the viewpoint of parameterized complexity and approximation algorithms. We contribute to this line of research by providing refined computational tractability as well as hardness results. We achieve this by a systematic investigation of various problem-specific parameters and their influence on the computational complexity. Charting the border between tractability and intractability, we also identify numerous challenges for future research.
Robust and Approximately Stable Marriages under Partial Information
Venue: arXiv cs.GT 2018
Year: 2018

We study the stable marriage problem in the partial information setting where the agents, although they have an underlying true strict linear order, are allowed to specify partial orders. Specifically, we focus on the case where the agents are allowed to submit strict weak orders and we try to address the following questions from the perspective of a market-designer: i) How can a designer generate matchings that are robust? ii) What is the trade-off between the amount of missing information and the "quality" of solution one can get? With the goal of resolving these questions through a simple and prior-free approach, we suggest looking at matchings that minimize the maximum number of blocking pairs with respect to all the possible underlying true orders as a measure of "quality", and subsequently provide results on finding such matchings. In particular, we first restrict our attention to matchings that have to be stable with respect to at least one of the completions and show that in this case arbitrarily filling-in the missing information and computing the resulting stable matching can give a non-trivial approximation factor for our problem in certain cases. We complement this result by showing that, even under severe restrictions on the preferences of the agents, the factor obtained is asymptotically tight in many cases. We then investigate a special case, where only agents on one side provide strict weak orders and all the missing information is at the bottom of their preference orders, and show that here the negative result mentioned above can be circumvented in order to get a much better approximation factor; this result, too, is tight in many cases. Finally, we move away from the restriction mentioned above and show a general hardness of approximation result and also discuss one possible approach that can lead us to a near-tight approximation bound.
A Non-Invasive Method for the Safe Interaction of Cities and Electric Vehicle Fleets
Venue: arXiv cs.SY 2018
Year: 2018

Electric and hybrid vehicles are growing in popularity. While these vehicles produce less pollution, they also produce less audible noise, especially at lower speeds. This makes it harder for pedestrians and cyclists to detect an approaching vehicle. Thus, an additional system is required to detect electric and hybrid vehicles and alert pedestrians and cyclists of their whereabouts, especially while these vehicles are driving at low speeds in cities. This paper introduces one such method based on high frequency audio emissions that are present in EVs, which arise, for example, from the process of magnetostriction. Our method is tested experimentally using 4 different tests vehicles, and a preliminary EV detection algorithm is also presented.
Automated Mouse Organ Segmentation: A Deep Learning Based Solution
Venue: arXiv cs.CV 2018
Year: 2018

The analysis of animal cross section images, such as cross sections of laboratory mice, is critical in assessing the effect of experimental drugs such as the biodistribution of candidate compounds in preclinical drug development stage. Tissue distribution of radiolabeled candidate therapeutic compounds can be quantified using techniques like Quantitative Whole-Body Autoradiography (QWBA).QWBA relies, among other aspects, on the accurate segmentation or identification of key organs of interest in the animal cross section image such as the brain, spine, heart, liver and others. We present a deep learning based organ segmentation solution to this problem, using which we can achieve automated organ segmentation with high precision (dice coefficient in the 0.83-0.95 range depending on organ) for the key organs of interest.
Secrecy Analysis of Physical Layer over $κ-μ$ Shadowed Fading Scenarios
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, the secrecy analysis of physical layer when both the main and wiretap channels undergo $κ-μ$ shadowed fading channel is investigated. In particular, the average secrecy capacity (ASC), secure outage probability (SOP), the lower bound of SOP (SOP$^L$), and the probability of strictly positive secrecy capacity (SPSC) are derived by using the classic Wyner's wiretap model. Two different scenarios for the fading parameters, i.e., $μ$ and $m$ which represents the shadowing impact have been studied. These parameters are chosen first as arbitrary numbers, thus the performance metrics are expressed in single infinite series with multivariate Meijer $G$-function. In the second scenario, both the aforementioned fading parameters are assumed to be integer numbers in order to obtain the derived results in simple exact closed-form analytic mathematically tractable expressions. The numerical results of this analysis are verified via Monte Carlo simulations.
On the effectiveness of task granularity for transfer learning
Venue: arXiv cs.CV 2018
Year: 2018

We describe a DNN for video classification and captioning, trained end-to-end, with shared features, to solve tasks at different levels of granularity, exploring the link between granularity in a source task and the quality of learned features for transfer learning. For solving the new task domain in transfer learning, we freeze the trained encoder and fine-tune a neural net on the target domain. We train on the Something-Something dataset with over 220, 000 videos, and multiple levels of target granularity, including 50 action groups, 174 fine-grained action categories and captions. Classification and captioning with Something-Something are challenging because of the subtle differences between actions, applied to thousands of different object classes, and the diversity of captions penned by crowd actors. Our model performs better than existing classification baselines for SomethingSomething, with impressive fine-grained results. And it yields a strong baseline on the new Something-Something captioning task. Experiments reveal that training with more fine-grained tasks tends to produce better features for transfer learning.